Overview

Dataset statistics

Number of variables18
Number of observations100
Missing cells218
Missing cells (%)12.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory14.2 KiB
Average record size in memory145.3 B

Variable types

Numeric11
Text2
Unsupported2
Categorical2
DateTime1

Alerts

id is highly overall correlated with host_idHigh correlation
host_id is highly overall correlated with idHigh correlation
latitude is highly overall correlated with longitude and 2 other fieldsHigh correlation
longitude is highly overall correlated with latitude and 1 other fieldsHigh correlation
number_of_reviews is highly overall correlated with reviews_per_month and 1 other fieldsHigh correlation
reviews_per_month is highly overall correlated with number_of_reviews and 1 other fieldsHigh correlation
number_of_reviews_ltm is highly overall correlated with number_of_reviews and 1 other fieldsHigh correlation
neighbourhood is highly overall correlated with latitude and 1 other fieldsHigh correlation
room_type is highly overall correlated with latitudeHigh correlation
neighbourhood_group has 100 (100.0%) missing valuesMissing
last_review has 9 (9.0%) missing valuesMissing
reviews_per_month has 9 (9.0%) missing valuesMissing
license has 100 (100.0%) missing valuesMissing
id has unique valuesUnique
neighbourhood_group is an unsupported type, check if it needs cleaning or further analysisUnsupported
license is an unsupported type, check if it needs cleaning or further analysisUnsupported
number_of_reviews has 9 (9.0%) zerosZeros
availability_365 has 9 (9.0%) zerosZeros
number_of_reviews_ltm has 23 (23.0%) zerosZeros

Reproduction

Analysis started2023-10-29 18:07:56.949571
Analysis finished2023-10-29 18:08:58.432604
Duration1 minute and 1.48 second
Software versionydata-profiling vv4.6.1
Download configurationconfig.json

Variables

id
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean147795.23
Minimum17878
Maximum262466
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size932.0 B
2023-10-29T15:08:58.983613image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum17878
5-th percentile49165.1
Q189885.25
median139388.5
Q3204501.25
95-th percentile249348.95
Maximum262466
Range244588
Interquartile range (IQR)114616

Descriptive statistics

Standard deviation66244.816
Coefficient of variation (CV)0.44822026
Kurtosis-1.1578439
Mean147795.23
Median Absolute Deviation (MAD)52018.5
Skewness0.061946276
Sum14779523
Variance4.3883757 × 109
MonotonicityStrictly increasing
2023-10-29T15:08:59.781624image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
17878 1
 
1.0%
173709 1
 
1.0%
200568 1
 
1.0%
198665 1
 
1.0%
191955 1
 
1.0%
190906 1
 
1.0%
188155 1
 
1.0%
186604 1
 
1.0%
178951 1
 
1.0%
178833 1
 
1.0%
Other values (90) 90
90.0%
ValueCountFrequency (%)
17878 1
1.0%
25026 1
1.0%
35764 1
1.0%
48305 1
1.0%
48901 1
1.0%
49179 1
1.0%
51703 1
1.0%
53533 1
1.0%
60718 1
1.0%
64795 1
1.0%
ValueCountFrequency (%)
262466 1
1.0%
257618 1
1.0%
256323 1
1.0%
251868 1
1.0%
249842 1
1.0%
249323 1
1.0%
248756 1
1.0%
247779 1
1.0%
247052 1
1.0%
245951 1
1.0%

name
Text

Distinct94
Distinct (%)94.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
2023-10-29T15:09:00.316631image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Length

Max length83
Median length70
Mean length64.65
Min length42

Characters and Unicode

Total characters6465
Distinct characters51
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique88 ?
Unique (%)88.0%

Sample

1st rowCondo in Rio de Janeiro · ★4.70 · 2 bedrooms · 2 beds · 1 bath
2nd rowRental unit in Rio de Janeiro · ★4.71 · 1 bedroom · 1 bed · 1 bath
3rd rowLoft in Rio de Janeiro · ★4.90 · 1 bedroom · 1 bed · 1.5 baths
4th rowRental unit in Ipanema · ★4.74 · 6 bedrooms · 7 beds · 7 baths
5th rowRental unit in Rio · ★4.37 · 4 bedrooms · 5 beds · 3 baths
ValueCountFrequency (%)
· 387
23.8%
1 170
 
10.4%
in 100
 
6.1%
rio 91
 
5.6%
de 83
 
5.1%
janeiro 83
 
5.1%
unit 76
 
4.7%
rental 76
 
4.7%
bedroom 64
 
3.9%
bath 64
 
3.9%
Other values (86) 434
26.7%
2023-10-29T15:09:01.603652image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1529
23.7%
e 470
 
7.3%
o 396
 
6.1%
· 387
 
6.0%
i 365
 
5.6%
n 349
 
5.4%
a 304
 
4.7%
d 300
 
4.6%
b 292
 
4.5%
t 277
 
4.3%
Other values (41) 1796
27.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 3499
54.1%
Space Separator 1529
23.7%
Decimal Number 565
 
8.7%
Other Punctuation 488
 
7.5%
Uppercase Letter 294
 
4.5%
Other Symbol 90
 
1.4%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 470
13.4%
o 396
11.3%
i 365
10.4%
n 349
10.0%
a 304
8.7%
d 300
8.6%
b 292
8.3%
t 277
7.9%
r 197
5.6%
s 135
 
3.9%
Other values (14) 414
11.8%
Uppercase Letter
ValueCountFrequency (%)
R 167
56.8%
J 84
28.6%
S 10
 
3.4%
H 8
 
2.7%
L 7
 
2.4%
C 7
 
2.4%
T 3
 
1.0%
I 2
 
0.7%
B 2
 
0.7%
P 1
 
0.3%
Other values (3) 3
 
1.0%
Decimal Number
ValueCountFrequency (%)
1 191
33.8%
4 108
19.1%
2 71
 
12.6%
7 40
 
7.1%
5 37
 
6.5%
8 34
 
6.0%
3 26
 
4.6%
6 23
 
4.1%
0 21
 
3.7%
9 14
 
2.5%
Other Punctuation
ValueCountFrequency (%)
· 387
79.3%
. 101
 
20.7%
Space Separator
ValueCountFrequency (%)
1529
100.0%
Other Symbol
ValueCountFrequency (%)
90
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 3793
58.7%
Common 2672
41.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 470
12.4%
o 396
10.4%
i 365
9.6%
n 349
9.2%
a 304
8.0%
d 300
7.9%
b 292
7.7%
t 277
7.3%
r 197
 
5.2%
R 167
 
4.4%
Other values (27) 676
17.8%
Common
ValueCountFrequency (%)
1529
57.2%
· 387
 
14.5%
1 191
 
7.1%
4 108
 
4.0%
. 101
 
3.8%
90
 
3.4%
2 71
 
2.7%
7 40
 
1.5%
5 37
 
1.4%
8 34
 
1.3%
Other values (4) 84
 
3.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 5986
92.6%
None 389
 
6.0%
Misc Symbols 90
 
1.4%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1529
25.5%
e 470
 
7.9%
o 396
 
6.6%
i 365
 
6.1%
n 349
 
5.8%
a 304
 
5.1%
d 300
 
5.0%
b 292
 
4.9%
t 277
 
4.6%
r 197
 
3.3%
Other values (37) 1507
25.2%
None
ValueCountFrequency (%)
· 387
99.5%
ç 1
 
0.3%
á 1
 
0.3%
Misc Symbols
ValueCountFrequency (%)
90
100.0%

host_id
Real number (ℝ)

HIGH CORRELATION 

Distinct88
Distinct (%)88.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean759383.9
Minimum68997
Maximum6416134
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size932.0 B
2023-10-29T15:09:02.252661image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum68997
5-th percentile109643.9
Q1475676.75
median645419.5
Q3904996.5
95-th percentile1322631.2
Maximum6416134
Range6347137
Interquartile range (IQR)429319.75

Descriptive statistics

Standard deviation685168.24
Coefficient of variation (CV)0.90226858
Kurtosis47.132344
Mean759383.9
Median Absolute Deviation (MAD)187383.5
Skewness5.8482637
Sum75938390
Variance4.6945551 × 1011
MonotonicityNot monotonic
2023-10-29T15:09:02.843669image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
792218 6
 
6.0%
474221 4
 
4.0%
1207700 3
 
3.0%
70933 2
 
2.0%
856145 2
 
2.0%
68997 1
 
1.0%
842466 1
 
1.0%
980805 1
 
1.0%
969899 1
 
1.0%
929229 1
 
1.0%
Other values (78) 78
78.0%
ValueCountFrequency (%)
68997 1
1.0%
70933 2
2.0%
93005 1
1.0%
102840 1
1.0%
110002 1
1.0%
153691 1
1.0%
222884 1
1.0%
224192 1
1.0%
235496 1
1.0%
238091 1
1.0%
ValueCountFrequency (%)
6416134 1
1.0%
2182548 1
1.0%
1377161 1
1.0%
1355685 1
1.0%
1348172 1
1.0%
1321287 1
1.0%
1309444 1
1.0%
1306210 1
1.0%
1298591 1
1.0%
1295841 1
1.0%
Distinct87
Distinct (%)87.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
2023-10-29T15:09:04.355690image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Length

Max length24
Median length16
Mean length7.53
Min length3

Characters and Unicode

Total characters753
Distinct characters56
Distinct categories7 ?
Distinct scripts3 ?
Distinct blocks4 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique81 ?
Unique (%)81.0%

Sample

1st rowMatthias
2nd rowViviane
3rd rowPatricia Miranda & Paulo
4th rowGoitaca
5th rowMarcio
ValueCountFrequency (%)
levy 6
 
4.5%
6
 
4.5%
june 4
 
3.0%
e 4
 
3.0%
maria 3
 
2.2%
luiza 3
 
2.2%
casa 2
 
1.5%
da 2
 
1.5%
❤️ 2
 
1.5%
ana 2
 
1.5%
Other values (95) 100
74.6%
2023-10-29T15:09:05.950715image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 123
16.3%
i 65
 
8.6%
e 58
 
7.7%
r 52
 
6.9%
n 47
 
6.2%
o 41
 
5.4%
l 37
 
4.9%
35
 
4.6%
s 22
 
2.9%
t 20
 
2.7%
Other values (46) 253
33.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 580
77.0%
Uppercase Letter 126
 
16.7%
Space Separator 35
 
4.6%
Other Punctuation 6
 
0.8%
Other Symbol 2
 
0.3%
Nonspacing Mark 2
 
0.3%
Decimal Number 2
 
0.3%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 123
21.2%
i 65
11.2%
e 58
10.0%
r 52
9.0%
n 47
 
8.1%
o 41
 
7.1%
l 37
 
6.4%
s 22
 
3.8%
t 20
 
3.4%
u 19
 
3.3%
Other values (16) 96
16.6%
Uppercase Letter
ValueCountFrequency (%)
L 13
 
10.3%
M 13
 
10.3%
C 13
 
10.3%
A 11
 
8.7%
R 10
 
7.9%
J 8
 
6.3%
G 7
 
5.6%
E 6
 
4.8%
S 6
 
4.8%
D 6
 
4.8%
Other values (13) 33
26.2%
Other Punctuation
ValueCountFrequency (%)
& 5
83.3%
, 1
 
16.7%
Decimal Number
ValueCountFrequency (%)
4 1
50.0%
8 1
50.0%
Space Separator
ValueCountFrequency (%)
35
100.0%
Other Symbol
ValueCountFrequency (%)
2
100.0%
Nonspacing Mark
ValueCountFrequency (%)
2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 706
93.8%
Common 45
 
6.0%
Inherited 2
 
0.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 123
17.4%
i 65
 
9.2%
e 58
 
8.2%
r 52
 
7.4%
n 47
 
6.7%
o 41
 
5.8%
l 37
 
5.2%
s 22
 
3.1%
t 20
 
2.8%
u 19
 
2.7%
Other values (39) 222
31.4%
Common
ValueCountFrequency (%)
35
77.8%
& 5
 
11.1%
2
 
4.4%
, 1
 
2.2%
4 1
 
2.2%
8 1
 
2.2%
Inherited
ValueCountFrequency (%)
2
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 747
99.2%
Dingbats 2
 
0.3%
VS 2
 
0.3%
None 2
 
0.3%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 123
16.5%
i 65
 
8.7%
e 58
 
7.8%
r 52
 
7.0%
n 47
 
6.3%
o 41
 
5.5%
l 37
 
5.0%
35
 
4.7%
s 22
 
2.9%
t 20
 
2.7%
Other values (42) 247
33.1%
Dingbats
ValueCountFrequency (%)
2
100.0%
VS
ValueCountFrequency (%)
2
100.0%
None
ValueCountFrequency (%)
â 1
50.0%
á 1
50.0%

neighbourhood_group
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing100
Missing (%)100.0%
Memory size932.0 B

neighbourhood
Categorical

HIGH CORRELATION 

Distinct25
Distinct (%)25.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
Copacabana
43 
Ipanema
12 
Santa Teresa
Leblon
 
4
Botafogo
 
4
Other values (20)
30 

Length

Max length24
Median length15
Mean length9.36
Min length3

Characters and Unicode

Total characters936
Distinct characters39
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique13 ?
Unique (%)13.0%

Sample

1st rowCopacabana
2nd rowCopacabana
3rd rowCopacabana
4th rowIpanema
5th rowCopacabana

Common Values

ValueCountFrequency (%)
Copacabana 43
43.0%
Ipanema 12
 
12.0%
Santa Teresa 7
 
7.0%
Leblon 4
 
4.0%
Botafogo 4
 
4.0%
Barra da Tijuca 3
 
3.0%
Laranjeiras 3
 
3.0%
Jacarepaguá 3
 
3.0%
Centro 2
 
2.0%
Flamengo 2
 
2.0%
Other values (15) 17
 
17.0%

Length

2023-10-29T15:09:06.442722image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
copacabana 43
36.1%
ipanema 12
 
10.1%
santa 7
 
5.9%
teresa 7
 
5.9%
leblon 4
 
3.4%
botafogo 4
 
3.4%
tijuca 4
 
3.4%
barra 3
 
2.5%
da 3
 
2.5%
laranjeiras 3
 
2.5%
Other values (23) 29
24.4%

Most occurring characters

ValueCountFrequency (%)
a 274
29.3%
n 80
 
8.5%
o 71
 
7.6%
p 58
 
6.2%
c 53
 
5.7%
e 52
 
5.6%
b 47
 
5.0%
C 46
 
4.9%
r 32
 
3.4%
t 20
 
2.1%
Other values (29) 203
21.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 802
85.7%
Uppercase Letter 115
 
12.3%
Space Separator 19
 
2.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 274
34.2%
n 80
 
10.0%
o 71
 
8.9%
p 58
 
7.2%
c 53
 
6.6%
e 52
 
6.5%
b 47
 
5.9%
r 32
 
4.0%
t 20
 
2.5%
m 19
 
2.4%
Other values (15) 96
 
12.0%
Uppercase Letter
ValueCountFrequency (%)
C 46
40.0%
I 13
 
11.3%
T 11
 
9.6%
S 10
 
8.7%
B 9
 
7.8%
L 9
 
7.8%
J 5
 
4.3%
G 4
 
3.5%
F 2
 
1.7%
H 2
 
1.7%
Other values (3) 4
 
3.5%
Space Separator
ValueCountFrequency (%)
19
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 917
98.0%
Common 19
 
2.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 274
29.9%
n 80
 
8.7%
o 71
 
7.7%
p 58
 
6.3%
c 53
 
5.8%
e 52
 
5.7%
b 47
 
5.1%
C 46
 
5.0%
r 32
 
3.5%
t 20
 
2.2%
Other values (28) 184
20.1%
Common
ValueCountFrequency (%)
19
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 923
98.6%
None 13
 
1.4%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 274
29.7%
n 80
 
8.7%
o 71
 
7.7%
p 58
 
6.3%
c 53
 
5.7%
e 52
 
5.6%
b 47
 
5.1%
C 46
 
5.0%
r 32
 
3.5%
t 20
 
2.2%
Other values (24) 190
20.6%
None
ValueCountFrequency (%)
á 9
69.2%
ú 1
 
7.7%
ç 1
 
7.7%
â 1
 
7.7%
ó 1
 
7.7%

latitude
Real number (ℝ)

HIGH CORRELATION 

Distinct99
Distinct (%)99.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-22.967203
Minimum-23.03154
Maximum-22.895768
Zeros0
Zeros (%)0.0%
Negative100
Negative (%)100.0%
Memory size932.0 B
2023-10-29T15:09:07.361743image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum-23.03154
5-th percentile-22.993978
Q1-22.982473
median-22.97671
Q3-22.960903
95-th percentile-22.914793
Maximum-22.895768
Range0.1357721
Interquartile range (IQR)0.02157

Descriptive statistics

Standard deviation0.026289685
Coefficient of variation (CV)-0.001144662
Kurtosis0.66339654
Mean-22.967203
Median Absolute Deviation (MAD)0.008865
Skewness0.93535993
Sum-2296.7203
Variance0.00069114754
MonotonicityNot monotonic
2023-10-29T15:09:07.915751image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-22.98114 2
 
2.0%
-22.96599 1
 
1.0%
-22.96104 1
 
1.0%
-22.98586 1
 
1.0%
-22.96055 1
 
1.0%
-22.95644 1
 
1.0%
-22.98057 1
 
1.0%
-22.96775 1
 
1.0%
-22.90544 1
 
1.0%
-22.98068 1
 
1.0%
Other values (89) 89
89.0%
ValueCountFrequency (%)
-23.03154 1
1.0%
-23.01124 1
1.0%
-23.0102 1
1.0%
-23.00809 1
1.0%
-23.00744 1
1.0%
-22.99327 1
1.0%
-22.98812 1
1.0%
-22.98752 1
1.0%
-22.98697 1
1.0%
-22.9869 1
1.0%
ValueCountFrequency (%)
-22.8957679 1
1.0%
-22.90022 1
1.0%
-22.90544 1
1.0%
-22.90559 1
1.0%
-22.91258 1
1.0%
-22.91491 1
1.0%
-22.91666 1
1.0%
-22.9172 1
1.0%
-22.91742 1
1.0%
-22.91882 1
1.0%

longitude
Real number (ℝ)

HIGH CORRELATION 

Distinct98
Distinct (%)98.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-43.214766
Minimum-43.49074
Maximum-43.16858
Zeros0
Zeros (%)0.0%
Negative100
Negative (%)100.0%
Memory size932.0 B
2023-10-29T15:09:08.571759image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum-43.49074
5-th percentile-43.383467
Q1-43.202998
median-43.19107
Q3-43.186512
95-th percentile-43.175127
Maximum-43.16858
Range0.32216
Interquartile range (IQR)0.016485

Descriptive statistics

Standard deviation0.066156204
Coefficient of variation (CV)-0.0015308703
Kurtosis6.7430083
Mean-43.214766
Median Absolute Deviation (MAD)0.00808
Skewness-2.7233276
Sum-4321.4766
Variance0.0043766433
MonotonicityNot monotonic
2023-10-29T15:09:09.618774image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-43.18969 2
 
2.0%
-43.18902 2
 
2.0%
-43.1794 1
 
1.0%
-43.17786 1
 
1.0%
-43.22285 1
 
1.0%
-43.19992 1
 
1.0%
-43.1995 1
 
1.0%
-43.18457 1
 
1.0%
-43.19471 1
 
1.0%
-43.191158 1
 
1.0%
Other values (88) 88
88.0%
ValueCountFrequency (%)
-43.49074 1
1.0%
-43.47437 1
1.0%
-43.43045 1
1.0%
-43.41337 1
1.0%
-43.388355 1
1.0%
-43.38321 1
1.0%
-43.37246 1
1.0%
-43.37081 1
1.0%
-43.3538124 1
1.0%
-43.31505 1
1.0%
ValueCountFrequency (%)
-43.16858 1
1.0%
-43.17442 1
1.0%
-43.17444341 1
1.0%
-43.17478 1
1.0%
-43.17488 1
1.0%
-43.17514 1
1.0%
-43.17563 1
1.0%
-43.17647 1
1.0%
-43.17676 1
1.0%
-43.17757 1
1.0%

room_type
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
Entire home/apt
71 
Private room
29 

Length

Max length15
Median length15
Mean length14.13
Min length12

Characters and Unicode

Total characters1413
Distinct characters15
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowEntire home/apt
2nd rowEntire home/apt
3rd rowEntire home/apt
4th rowEntire home/apt
5th rowEntire home/apt

Common Values

ValueCountFrequency (%)
Entire home/apt 71
71.0%
Private room 29
29.0%

Length

2023-10-29T15:09:10.252785image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-29T15:09:10.828793image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
entire 71
35.5%
home/apt 71
35.5%
private 29
14.5%
room 29
14.5%

Most occurring characters

ValueCountFrequency (%)
t 171
12.1%
e 171
12.1%
r 129
9.1%
o 129
9.1%
i 100
 
7.1%
100
 
7.1%
m 100
 
7.1%
a 100
 
7.1%
E 71
 
5.0%
n 71
 
5.0%
Other values (5) 271
19.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 1142
80.8%
Space Separator 100
 
7.1%
Uppercase Letter 100
 
7.1%
Other Punctuation 71
 
5.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
t 171
15.0%
e 171
15.0%
r 129
11.3%
o 129
11.3%
i 100
8.8%
m 100
8.8%
a 100
8.8%
n 71
6.2%
h 71
6.2%
p 71
6.2%
Uppercase Letter
ValueCountFrequency (%)
E 71
71.0%
P 29
29.0%
Space Separator
ValueCountFrequency (%)
100
100.0%
Other Punctuation
ValueCountFrequency (%)
/ 71
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 1242
87.9%
Common 171
 
12.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
t 171
13.8%
e 171
13.8%
r 129
10.4%
o 129
10.4%
i 100
8.1%
m 100
8.1%
a 100
8.1%
E 71
5.7%
n 71
5.7%
h 71
5.7%
Other values (3) 129
10.4%
Common
ValueCountFrequency (%)
100
58.5%
/ 71
41.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1413
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
t 171
12.1%
e 171
12.1%
r 129
9.1%
o 129
9.1%
i 100
 
7.1%
100
 
7.1%
m 100
 
7.1%
a 100
 
7.1%
E 71
 
5.0%
n 71
 
5.0%
Other values (5) 271
19.2%

price
Real number (ℝ)

Distinct81
Distinct (%)81.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean784.22
Minimum59
Maximum25000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size932.0 B
2023-10-29T15:09:11.446802image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum59
5-th percentile130
Q1180
median263.5
Q3542.25
95-th percentile2459.4
Maximum25000
Range24941
Interquartile range (IQR)362.25

Descriptive statistics

Standard deviation2613.3744
Coefficient of variation (CV)3.3324506
Kurtosis76.4909
Mean784.22
Median Absolute Deviation (MAD)101.5
Skewness8.3876731
Sum78422
Variance6829726
MonotonicityNot monotonic
2023-10-29T15:09:12.158813image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
130 4
 
4.0%
200 3
 
3.0%
180 3
 
3.0%
736 3
 
3.0%
300 3
 
3.0%
220 2
 
2.0%
345 2
 
2.0%
164 2
 
2.0%
140 2
 
2.0%
193 2
 
2.0%
Other values (71) 74
74.0%
ValueCountFrequency (%)
59 1
 
1.0%
101 1
 
1.0%
107 1
 
1.0%
126 1
 
1.0%
130 4
4.0%
133 1
 
1.0%
140 2
2.0%
142 1
 
1.0%
145 1
 
1.0%
147 1
 
1.0%
ValueCountFrequency (%)
25000 1
1.0%
7381 1
1.0%
3464 1
1.0%
3448 1
1.0%
2600 1
1.0%
2452 1
1.0%
2054 1
1.0%
1278 1
1.0%
1220 1
1.0%
1107 1
1.0%

minimum_nights
Real number (ℝ)

Distinct12
Distinct (%)12.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.33
Minimum1
Maximum60
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size932.0 B
2023-10-29T15:09:12.653819image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median3
Q33
95-th percentile14.35
Maximum60
Range59
Interquartile range (IQR)1

Descriptive statistics

Standard deviation7.5009158
Coefficient of variation (CV)1.7323131
Kurtosis32.963639
Mean4.33
Median Absolute Deviation (MAD)1
Skewness5.297872
Sum433
Variance56.263737
MonotonicityNot monotonic
2023-10-29T15:09:13.070825image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
2 33
33.0%
3 31
31.0%
1 14
14.0%
5 6
 
6.0%
4 6
 
6.0%
28 3
 
3.0%
6 2
 
2.0%
8 1
 
1.0%
60 1
 
1.0%
21 1
 
1.0%
Other values (2) 2
 
2.0%
ValueCountFrequency (%)
1 14
14.0%
2 33
33.0%
3 31
31.0%
4 6
 
6.0%
5 6
 
6.0%
6 2
 
2.0%
7 1
 
1.0%
8 1
 
1.0%
14 1
 
1.0%
21 1
 
1.0%
ValueCountFrequency (%)
60 1
 
1.0%
28 3
 
3.0%
21 1
 
1.0%
14 1
 
1.0%
8 1
 
1.0%
7 1
 
1.0%
6 2
 
2.0%
5 6
 
6.0%
4 6
 
6.0%
3 31
31.0%

number_of_reviews
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct73
Distinct (%)73.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean95.84
Minimum0
Maximum540
Zeros9
Zeros (%)9.0%
Negative0
Negative (%)0.0%
Memory size932.0 B
2023-10-29T15:09:13.501832image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q110
median77
Q3139.25
95-th percentile282.95
Maximum540
Range540
Interquartile range (IQR)129.25

Descriptive statistics

Standard deviation107.56372
Coefficient of variation (CV)1.122326
Kurtosis4.1882984
Mean95.84
Median Absolute Deviation (MAD)67
Skewness1.8627065
Sum9584
Variance11569.954
MonotonicityNot monotonic
2023-10-29T15:09:14.518847image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 9
 
9.0%
4 6
 
6.0%
6 3
 
3.0%
37 3
 
3.0%
106 2
 
2.0%
87 2
 
2.0%
226 2
 
2.0%
3 2
 
2.0%
42 2
 
2.0%
9 2
 
2.0%
Other values (63) 67
67.0%
ValueCountFrequency (%)
0 9
9.0%
2 1
 
1.0%
3 2
 
2.0%
4 6
6.0%
5 1
 
1.0%
6 3
 
3.0%
9 2
 
2.0%
10 2
 
2.0%
13 1
 
1.0%
14 1
 
1.0%
ValueCountFrequency (%)
540 1
1.0%
458 1
1.0%
446 1
1.0%
421 1
1.0%
301 1
1.0%
282 1
1.0%
272 1
1.0%
250 1
1.0%
230 1
1.0%
226 2
2.0%

last_review
Date

MISSING 

Distinct64
Distinct (%)70.3%
Missing9
Missing (%)9.0%
Memory size932.0 B
Minimum2016-01-05 00:00:00
Maximum2023-09-21 00:00:00
2023-10-29T15:09:15.101855image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-29T15:09:15.854865image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

reviews_per_month
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct68
Distinct (%)74.7%
Missing9
Missing (%)9.0%
Infinite0
Infinite (%)0.0%
Mean0.76747253
Minimum0.02
Maximum3.79
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size932.0 B
2023-10-29T15:09:16.370874image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0.02
5-th percentile0.04
Q10.2
median0.61
Q31.07
95-th percentile1.86
Maximum3.79
Range3.77
Interquartile range (IQR)0.87

Descriptive statistics

Standard deviation0.73597342
Coefficient of variation (CV)0.95895735
Kurtosis3.9452098
Mean0.76747253
Median Absolute Deviation (MAD)0.44
Skewness1.7451642
Sum69.84
Variance0.54165687
MonotonicityNot monotonic
2023-10-29T15:09:16.864880image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.04 5
 
5.0%
0.65 4
 
4.0%
0.06 3
 
3.0%
0.61 3
 
3.0%
0.3 3
 
3.0%
0.57 2
 
2.0%
0.14 2
 
2.0%
0.1 2
 
2.0%
0.02 2
 
2.0%
1.48 2
 
2.0%
Other values (58) 63
63.0%
(Missing) 9
 
9.0%
ValueCountFrequency (%)
0.02 2
 
2.0%
0.03 1
 
1.0%
0.04 5
5.0%
0.05 1
 
1.0%
0.06 3
3.0%
0.07 1
 
1.0%
0.08 1
 
1.0%
0.1 2
 
2.0%
0.12 1
 
1.0%
0.13 2
 
2.0%
ValueCountFrequency (%)
3.79 1
1.0%
3.14 1
1.0%
3.06 1
1.0%
2.82 1
1.0%
1.87 1
1.0%
1.85 2
2.0%
1.68 1
1.0%
1.63 1
1.0%
1.62 1
1.0%
1.54 1
1.0%
Distinct12
Distinct (%)12.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.49
Minimum1
Maximum28
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size932.0 B
2023-10-29T15:09:17.699893image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median2
Q34
95-th percentile10
Maximum28
Range27
Interquartile range (IQR)3

Descriptive statistics

Standard deviation4.0514245
Coefficient of variation (CV)1.1608666
Kurtosis14.893469
Mean3.49
Median Absolute Deviation (MAD)1
Skewness3.2596058
Sum349
Variance16.41404
MonotonicityNot monotonic
2023-10-29T15:09:18.111900image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
1 40
40.0%
2 22
22.0%
6 7
 
7.0%
4 7
 
7.0%
3 7
 
7.0%
10 6
 
6.0%
9 3
 
3.0%
7 3
 
3.0%
5 2
 
2.0%
20 1
 
1.0%
Other values (2) 2
 
2.0%
ValueCountFrequency (%)
1 40
40.0%
2 22
22.0%
3 7
 
7.0%
4 7
 
7.0%
5 2
 
2.0%
6 7
 
7.0%
7 3
 
3.0%
8 1
 
1.0%
9 3
 
3.0%
10 6
 
6.0%
ValueCountFrequency (%)
28 1
 
1.0%
20 1
 
1.0%
10 6
6.0%
9 3
3.0%
8 1
 
1.0%
7 3
3.0%
6 7
7.0%
5 2
 
2.0%
4 7
7.0%
3 7
7.0%

availability_365
Real number (ℝ)

ZEROS 

Distinct74
Distinct (%)74.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean213.65
Minimum0
Maximum365
Zeros9
Zeros (%)9.0%
Negative0
Negative (%)0.0%
Memory size932.0 B
2023-10-29T15:09:18.718907image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q1112.75
median243.5
Q3321.25
95-th percentile365
Maximum365
Range365
Interquartile range (IQR)208.5

Descriptive statistics

Standard deviation122.85373
Coefficient of variation (CV)0.57502329
Kurtosis-1.1919049
Mean213.65
Median Absolute Deviation (MAD)84.5
Skewness-0.45734665
Sum21365
Variance15093.038
MonotonicityNot monotonic
2023-10-29T15:09:19.652921image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 9
 
9.0%
365 6
 
6.0%
282 2
 
2.0%
304 2
 
2.0%
170 2
 
2.0%
326 2
 
2.0%
277 2
 
2.0%
362 2
 
2.0%
309 2
 
2.0%
325 2
 
2.0%
Other values (64) 69
69.0%
ValueCountFrequency (%)
0 9
9.0%
3 1
 
1.0%
30 1
 
1.0%
34 1
 
1.0%
35 1
 
1.0%
40 1
 
1.0%
46 1
 
1.0%
49 1
 
1.0%
50 1
 
1.0%
51 1
 
1.0%
ValueCountFrequency (%)
365 6
6.0%
364 1
 
1.0%
363 2
 
2.0%
362 2
 
2.0%
359 1
 
1.0%
342 1
 
1.0%
341 1
 
1.0%
340 1
 
1.0%
335 1
 
1.0%
328 2
 
2.0%

number_of_reviews_ltm
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct33
Distinct (%)33.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10.81
Minimum0
Maximum68
Zeros23
Zeros (%)23.0%
Negative0
Negative (%)0.0%
Memory size932.0 B
2023-10-29T15:09:20.315932image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median8
Q317.25
95-th percentile33
Maximum68
Range68
Interquartile range (IQR)16.25

Descriptive statistics

Standard deviation12.446812
Coefficient of variation (CV)1.1514165
Kurtosis3.9435473
Mean10.81
Median Absolute Deviation (MAD)7.5
Skewness1.6842205
Sum1081
Variance154.92313
MonotonicityNot monotonic
2023-10-29T15:09:21.047941image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=33)
ValueCountFrequency (%)
0 23
23.0%
2 10
 
10.0%
8 9
 
9.0%
1 5
 
5.0%
25 4
 
4.0%
3 4
 
4.0%
13 3
 
3.0%
10 3
 
3.0%
12 3
 
3.0%
22 2
 
2.0%
Other values (23) 34
34.0%
ValueCountFrequency (%)
0 23
23.0%
1 5
 
5.0%
2 10
10.0%
3 4
 
4.0%
4 2
 
2.0%
5 2
 
2.0%
7 2
 
2.0%
8 9
 
9.0%
9 2
 
2.0%
10 3
 
3.0%
ValueCountFrequency (%)
68 1
 
1.0%
50 1
 
1.0%
37 1
 
1.0%
35 1
 
1.0%
33 2
2.0%
31 1
 
1.0%
30 2
2.0%
29 1
 
1.0%
28 1
 
1.0%
25 4
4.0%

license
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing100
Missing (%)100.0%
Memory size932.0 B

Interactions

2023-10-29T15:08:51.714506image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-29T15:07:59.300608image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-29T15:08:04.496681image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-29T15:08:10.327315image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-29T15:08:15.957978image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-29T15:08:21.933068image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-29T15:08:26.315128image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-29T15:08:31.717209image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-29T15:08:36.588279image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-29T15:08:41.755354image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-29T15:08:48.037318image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-29T15:08:52.147514image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-29T15:07:59.727612image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-29T15:08:04.888687image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-29T15:08:10.600885image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-29T15:08:16.544987image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-29T15:08:22.222069image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-29T15:08:26.543132image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-29T15:08:32.367217image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-29T15:08:37.083286image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-29T15:08:42.145369image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-29T15:08:48.348320image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-29T15:08:52.636520image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-29T15:08:00.126617image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-29T15:08:05.182692image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-29T15:08:11.203908image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-29T15:08:16.892992image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-29T15:08:22.817077image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-29T15:08:26.912139image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-29T15:08:32.895226image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-29T15:08:37.660294image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-29T15:08:42.693377image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-29T15:08:48.615191image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-29T15:08:52.949524image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-29T15:08:00.443622image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-29T15:08:05.469728image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-29T15:08:11.528913image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-29T15:08:17.387999image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-29T15:08:23.165083image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-29T15:08:27.536147image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-29T15:08:33.204229image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-29T15:08:37.979300image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-29T15:08:43.053382image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-29T15:08:48.868195image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-29T15:08:53.404531image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-29T15:08:00.769627image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-29T15:08:05.751700image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-29T15:08:12.150922image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-29T15:08:18.119008image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-29T15:08:23.472087image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-29T15:08:27.855152image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-29T15:08:33.781238image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-29T15:08:38.563308image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-29T15:08:43.561059image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-29T15:08:49.099350image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-29T15:08:53.849537image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-29T15:08:01.400637image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-29T15:08:05.992726image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-29T15:08:12.635928image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-29T15:08:18.770018image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-29T15:08:23.684091image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-29T15:08:29.415174image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-29T15:08:34.031242image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-29T15:08:38.835311image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-29T15:08:44.004067image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-29T15:08:49.347973image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-29T15:08:54.153541image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-29T15:08:01.992644image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-29T15:08:06.831715image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-29T15:08:13.265939image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-29T15:08:19.383028image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-29T15:08:24.089095image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-29T15:08:29.855181image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-29T15:08:34.373247image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-29T15:08:39.131316image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-29T15:08:44.329071image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-29T15:08:49.634983image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-29T15:08:54.416545image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-29T15:08:02.451651image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-29T15:08:07.561725image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-29T15:08:13.869948image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-29T15:08:19.987036image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-29T15:08:24.738106image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-29T15:08:30.135184image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-29T15:08:34.913254image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-29T15:08:39.418321image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-29T15:08:45.048151image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-29T15:08:49.881449image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-29T15:08:55.037554image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-29T15:08:03.216661image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-29T15:08:08.148734image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-29T15:08:14.254952image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-29T15:08:20.667047image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-29T15:08:25.103110image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-29T15:08:30.419188image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-29T15:08:35.292260image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-29T15:08:40.328334image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-29T15:08:45.944165image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-29T15:08:50.131457image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-29T15:08:55.484560image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-29T15:08:03.680669image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-29T15:08:08.501028image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-29T15:08:14.780960image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-29T15:08:21.130054image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-29T15:08:25.520117image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-29T15:08:30.702192image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-29T15:08:35.798268image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-29T15:08:40.816341image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-29T15:08:46.403172image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-29T15:08:50.639460image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-29T15:08:55.900568image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-29T15:08:04.081676image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-29T15:08:09.513042image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-29T15:08:15.512972image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-29T15:08:21.453057image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-29T15:08:26.036124image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-29T15:08:31.051198image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-29T15:08:36.133272image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-29T15:08:41.436350image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-29T15:08:47.260184image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-29T15:08:51.247982image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Correlations

2023-10-29T15:09:23.184975image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
idhost_idlatitudelongitudepriceminimum_nightsnumber_of_reviewsreviews_per_monthcalculated_host_listings_countavailability_365number_of_reviews_ltmneighbourhoodroom_type
id1.0000.8710.1840.220-0.142-0.052-0.091-0.0760.074-0.019-0.1120.1800.000
host_id0.8711.0000.2070.158-0.214-0.055-0.223-0.196-0.017-0.013-0.2410.4710.000
latitude0.1840.2071.0000.565-0.234-0.074-0.254-0.171-0.1560.019-0.2640.7800.519
longitude0.2200.1580.5651.000-0.2280.1170.0980.0690.026-0.1150.0890.7560.000
price-0.142-0.214-0.234-0.2281.000-0.039-0.134-0.075-0.2870.267-0.1250.0000.000
minimum_nights-0.052-0.055-0.0740.117-0.0391.0000.018-0.026-0.035-0.142-0.0590.4870.210
number_of_reviews-0.091-0.223-0.2540.098-0.1340.0181.0000.9340.255-0.1060.7860.0000.382
reviews_per_month-0.076-0.196-0.1710.069-0.075-0.0260.9341.0000.145-0.1460.7610.0000.383
calculated_host_listings_count0.074-0.017-0.1560.026-0.287-0.0350.2550.1451.000-0.0850.2410.0000.000
availability_365-0.019-0.0130.019-0.1150.267-0.142-0.106-0.146-0.0851.000-0.0400.0000.235
number_of_reviews_ltm-0.112-0.241-0.2640.089-0.125-0.0590.7860.7610.241-0.0401.0000.0000.341
neighbourhood0.1800.4710.7800.7560.0000.4870.0000.0000.0000.0000.0001.0000.455
room_type0.0000.0000.5190.0000.0000.2100.3820.3830.0000.2350.3410.4551.000

Missing values

2023-10-29T15:08:56.481576image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
A simple visualization of nullity by column.
2023-10-29T15:08:57.325588image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2023-10-29T15:08:58.131599image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

idnamehost_idhost_nameneighbourhood_groupneighbourhoodlatitudelongituderoom_typepriceminimum_nightsnumber_of_reviewslast_reviewreviews_per_monthcalculated_host_listings_countavailability_365number_of_reviews_ltmlicense
017878Condo in Rio de Janeiro · ★4.70 · 2 bedrooms · 2 beds · 1 bath68997MatthiasNaNCopacabana-22.965990-43.179400Entire home/apt27953012023-09-111.87126525NaN
125026Rental unit in Rio de Janeiro · ★4.71 · 1 bedroom · 1 bed · 1 bath102840VivianeNaNCopacabana-22.977350-43.191050Entire home/apt33022722023-09-071.68120324NaN
235764Loft in Rio de Janeiro · ★4.90 · 1 bedroom · 1 bed · 1.5 baths153691Patricia Miranda & PauloNaNCopacabana-22.981070-43.191360Entire home/apt19234462023-09-112.8214637NaN
348305Rental unit in Ipanema · ★4.74 · 6 bedrooms · 7 beds · 7 baths70933GoitacaNaNIpanema-22.985910-43.203020Entire home/apt344821522023-09-100.99930630NaN
448901Rental unit in Rio · ★4.37 · 4 bedrooms · 5 beds · 3 baths222884MarcioNaNCopacabana-22.965740-43.175140Entire home/apt7033202023-09-100.20130712NaN
549179Rental unit in Rio de Janeiro · ★4.81 · 1 bedroom · 1 bed · 1 bath224192DavidNaNCopacabana-22.979100-43.190080Entire home/apt20141472023-09-051.122015920NaN
651703Rental unit in Rio de Janeiro · ★4.75 · Studio · 1 bed · 1 bath238091DáliaNaNCopacabana-22.981731-43.190571Entire home/apt17832502023-08-281.85232130NaN
753533Home in Joatinga · ★4.94 · 4 bedrooms · 6 beds · 4 baths249439Sherri & AndreNaNJoá-23.008090-43.291130Entire home/apt12782342023-07-240.2413418NaN
860718Rental unit in Rio de Janeiro · ★4.78 · 4 bedrooms · 4 beds · 2 baths292870TâniaNaNFlamengo-22.929720-43.174880Entire home/apt6906102023-02-270.0613282NaN
964795Rental unit in Rio de Janeiro · ★4.75 · 3 bedrooms · 4 beds · 2.5 baths93005AndreaNaNIpanema-22.981690-43.202800Entire home/apt5433642023-08-150.42115622NaN
idnamehost_idhost_nameneighbourhood_groupneighbourhoodlatitudelongituderoom_typepriceminimum_nightsnumber_of_reviewslast_reviewreviews_per_monthcalculated_host_listings_countavailability_365number_of_reviews_ltmlicense
90245951Rental unit in Rio de Janeiro · ★4.86 · 1 bedroom · 2 beds · 1 bath1289982Carnaval TurismoNaNCopacabana-22.96704-43.18201Entire home/apt3003142023-06-110.1013421NaN
91247052Rental unit in Rio de Janeiro · 2 bedrooms · 2 beds · 3 baths1295841OsmarNaNBotafogo-22.95665-43.18481Entire home/apt73650NaNNaN130NaN
92247779Rental unit in Rio de Janeiro · ★4.75 · 1 bedroom · 1 bed · 1 bath1298591KarinaNaNCopacabana-22.98510-43.19320Entire home/apt17021472023-09-181.032577NaN
93248756Condo in Rio de Janeiro · ★4.76 · 2 bedrooms · 2 beds · 1.5 baths804664BernardoNaNCopacabana-22.98029-43.19054Entire home/apt35831542023-09-101.0614918NaN
94249323Rental unit in Rio de Janeiro · ★4.77 · 2 bedrooms · 2 beds · 2.5 baths1306210RaphaelNaNCopacabana-22.97600-43.18838Entire home/apt61611362023-09-070.94128231NaN
95249842Rental unit in Rio · ★4.88 · 1 bedroom · 1 bed · 1 shared bath1309444SoniaNaNCopacabana-22.96794-43.18969Private room13321762023-09-021.25310833NaN
96251868Rental unit in Rio de Janeiro · ★4.84 · 1 bedroom · 3 beds · 1 bath1321287HelenaNaNCopacabana-22.97920-43.19010Entire home/apt1304872023-02-210.612632NaN
97256323Guest suite in Santa Teresa · ★4.81 · 1 bedroom · 1 bed · 1 bath1348172JenniferNaNSanta Teresa-22.91742-43.18330Entire home/apt19532262023-09-091.62114015NaN
98257618Rental unit in Rio de Janeiro · ★4.73 · 1 bedroom · 1 bed · 1 private bath1355685JorgeNaNLeme-22.96102-43.16858Private room2092422022-09-120.2922120NaN
99262466Rental unit in Rio de Janeiro · ★5.0 · 1 bedroom · 1 bed · 1 bath1377161Ana CristinaNaNBotafogo-22.95508-43.18251Entire home/apt250132016-01-050.0211430NaN